Identification of QTLs linked to fruit quality traits in apricot (Prunus armeniaca L.) and biological validation through gene expression analysis using qPCR

Abstract

Nine important fruit quality traits-including fruit weight, stone weight, fruit diameter, skin ground colour, flesh colour, blush colour, firmness, soluble solids content and acidity content-were studied for two consecutive years in two F 1 apricot progeny derived from the crosses 'Bergeron' × 'Currot' (B×C) and 'Goldrich' × 'Currot' (G×C). Results showed great segregation variability between populations, which was expected because of the polygenic nature and quantitative inheritance of all the studied traits. In addition, some correlations were observed among the fruit quality traits studied. QTL (quantitative trait loci) analysis was carried out using the phenotypic data and genetic linkages maps of 'B×C' and 'G×C' obtained with SSR and SNP markers. The most significant QTLs were localised in LG4 for soluble solids content and in LG3 for skin and flesh colour. In LG4, we can highlight the presence of candidate genes involved in D-glucose and D-mannose 33 binding, while in LG3, we identified MYB genes previ-34 ously linked to skin colour by other authors. In order to 35 clearly identify the candidate genes responsible for the 36 analysed traits, we converted the QTLs into expression 37 QTLs and analysed the abundance of transcripts in the 38 segregating genotypes 'GC 2-11' and 'GC 3-7' from 39 the G×C population. Using qPCR, we analysed the gene 40 expression of nine candidate genes associated with the 41 QTLs identified, including transcription factors (MYB 42 10), carotenoid biosynthesis genes (LOX 2, CCD1 and 43 CCD4), anthocyanin biosynthesis genes (ANS, UFGT 44 and F3'5'H), organic acid biosynthesis genes (NAD 45 ME) and ripening date genes (NAC). Results showed 46 variable expression patterns throughout fruit develop-47 ment and between contrasted genotypes, with a correla-48 tion between validated genes and linked QTLs. The 49 MYB10 gene was the best candidate gene for skin col-50 our. In addition, we found that monitoring NAC expres-51 sion is a good RNA marker for evaluating ripening 52 progression.

The apricot (Prunus armeniaca L.) genome elucidates Rosaceae evolution and beta-carotenoid synthesis

“…The balance of the biosynthesis and decomposition of β-carotene may contribute to the color of apricot fruit. First, β-carotene, as one of the metabolites important for fruit quality, contributes to the yellow color of apricot fruit 67,68 . Among fruit pigments, anthocyanins are the main typical pigments contributing to the red, pink or violet coloration of some fruit, such as apple, tomato, purple sweet potato, and strawberry 69–76 .…”

Stone Fruit as Biofactories of Phytochemicals With Potential Roles in Human Nutrition and Health

“…The overexpression of this MYB transcription factor in fruits of Luntaixiaobaixing cultivar conducted the red coloration of the skin. PaMYB10 was found to be located with Linkage group 3 (G3) related to skin color ( García-Gómez et al., 2019 ). In addition, it was probed that anthocyanin accumulation in apricot was also influenced by light, as fruit bagging affects the coloration of the skin ( Xi et al., 2019 ).…”

Expression QTL (eQTLs) Analyses Reveal Candidate Genes Associated With Fruit Flesh Softening Rate in Peach [Prunus persica (L.) Batsch]

“…Considering that the sensory and nutritional characteristics defining fruit quality traits are determined at the ripening stage, the identification of genetic and molecular factors acting as key drivers of ripening will allow improvements to be made to the overall fruit quality (Carrari and Fernie, 2006; García-Gómez et al, 2019), as well as to preserve fruit quality attributes during the postharvest shelf life (Liu et al, 2015).…”

“…Several studies have focused on peach in efforts to identify associations between major genes, QTLs, and candidate genes for fruit quality traits (Etienne et al, 2002; Infante et al, 2008; Eduardo et al, 2011; Fresnedo-Ramírez et al, 2015), as well as in other Prunus species, such as apricot (Salazar et al, 2014; García-Gómez et al, 2019) and Japanese plum (Salazar et al, 2017); for a review, see Aranzana et al (2019).…”

“…Therefore, in order to determine candidate genes related to targeted traits, transcript abundances had been used as quantitative trait data in the QTL analysis (Druka et al, 2010; Galpaz et al, 2018; García-Gómez et al, 2019). Multiple authors have noted the usefulness of gene expression analysis based on qPCR for the experimental validation of QTLs in fruit crops (Huang et al, 2014; Sugiyama et al, 2014; García-Gómez et al, 2019), confirming the correlation between genotype and gene expression levels (Conesa et al, 2016).…”